Abstract Details

Axon Registry Data Validation: Accuracy Assessment of Data Extraction and Measure Specification
Practice, Policy, and Ethics
S50 - Practice, Policy, and Ethics (4:36 PM-4:47 PM)
007
The Axon Registry uses software technology that allows data elements to be automatically extracted from the electronic health record (EHR), delivered to a registry database and used for calculation of quality measures. Establishing the validity of the data extraction process for this technology is critical to ensuring that the quality performance data accurately reflects the quality of the documented care
To conduct a data validation study encompassing an accuracy assessment of the data extraction process for the Axon Registry.
Data elements were abstracted from EHRs by an external auditor (IQVIA) using virtual site-visits at participating sites. IQVIA independently calculated Axon Registry quality measure performance rates based on AAN measure specifications and logic using Axon Registry data. Agreement between Axon Registry and IQVIA data elements and measure performance rates was calculated. Discordance was investigated to elucidate underlying systemic or idiosyncratic reasons for disagreement.
Nine sites (n=720 patients; n=80 patients per site) with diversity among EHR vendor, practice settings, size, locations, and data transfer method were included. There was variable concordance between the data elements in the Axon Registry and those abstracted independently by IQVIA; high match rates (>92%) were observed for discrete elements; lower match rates (<44%) were observed for elements with free text. Across all measures, there was a 76% patient-level measure performance agreement between Axon Registry and IQVIA (k=0.53, P<0.001). 
There was a range of concordance between data elements and quality measures in the Axon Registry and those independently abstracted and calculated by an independent vendor. Validation of data and processes to ensure data accuracy are important for clinical quality data registries, particularly those utilizing automated data extraction methods from EHRs.  These findings provide an essential foundation upon which continual improvements in Axon Registry data extraction can be based.  
Authors/Disclosures
Lyell K. Jones, MD, FAAN (Mayo Clinic)
PRESENTER 		Dr. Jones has received personal compensation in the range of $100,000-$499,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for 好色先生. Dr. Jones has received publishing royalties from a publication relating to health care. Dr. Jones has a non-compensated relationship as a member of the AAN Board of Directors with AAN that is relevant to AAN interests or activities. Dr. Jones has a non-compensated relationship as a Chair of the Mayo Clinic ACO Board of Directors with Mayo Clinic that is relevant to AAN interests or activities.
Christine B. Baca, MD, FAAN (Virginia Commonwealth University) Dr. Baca has nothing to disclose.
Sarah M. Benish, MD, FAAN (University of Minnesota) Dr. Benish has nothing to disclose.
Aleksandar Videnovic, MD, MSc, FAAN (MGH Neurological Clinical Research Institute) Dr. Videnovic has nothing to disclose.
Karen Lundgren (好色先生) Ms. Lundgren has nothing to disclose.
Brandon Magliocco (好色先生) No disclosure on file
Becky Schierman (好色先生) Ms. Schierman has nothing to disclose.
Laura Palmer (University of Colorado) No disclosure on file